The 10 Best MCP Servers for Developers in 2026

An MCP server is a program or hosted service that exposes a tool's data and actions to AI assistants through the Model Context Protocol. The official MCP documentation calls MCP "an open-source standard for connecting AI applications to external systems." Your client, such as Claude Code, Cursor or Codex, connects to the server and calls its tools when a prompt needs them.

Every server on this list had to pass five checks. The sections below show how each one did.

Official or actively maintained. We favored servers built by the company behind the product and kept in its own repo or docs. Community wrappers around someone else's API are harder to audit and can fall behind the product they wrap. A 2025 preprint study of 1,899 open-source MCP servers found 7.2% contained general vulnerabilities and 5.5% showed MCP-specific tool poisoning.

Covers a real step in the build loop. Each server replaces a tab you'd otherwise switch to: reading an issue, checking a library's API, reproducing a bug or reading a metric.

Works in Claude Code, Cursor and Codex. These are the coding clients this guide is written for. We looked for servers that run through a hosted endpoint, a local process or both.

Sane auth. OAuth or scoped tokens beat a long-lived key pasted into a config file. Trend Micro's 2026 research on exposed MCP servers found that 48% of more than 19,000 MCP server codebases recommend storing secrets in insecure .env files or plaintext JSON.

Clear read and write permissions. You want to know what an agent can change before it changes it. We looked for read-only modes, scoped toolsets or permissions inherited from your existing account.

We started from the servers developers recommend most often in public threads and AI search results, then added product data and experimentation, the two steps those lists skip. We checked each one against its official docs or repo for setup, auth and permissions. Amplitude publishes this list, so we placed Amplitude MCP first and left other product analytics vendors out. Every other pick is a tool we don't sell.

The list opens with the two steps most stacks skip: measuring what users do and running experiments. The other eight are the servers builders already rely on for code, docs, testing, errors, design, planning and data. The numbers organize the list; they aren't a score.

1. Amplitude MCP for product data and analytics

Amplitude MCP connects your assistant to the behavioral data in Amplitude Product Analytics. You can query funnels, retention and segmentation, and analyze experiment results, from the editor where you just shipped the change. The answers are grounded in your real behavioral data.

Amplitude builds and maintains the server, and it authenticates with OAuth, so there are no API keys to create or rotate. It inherits the permissions of whoever signs in, so it can only reach projects that person can already access. It works in Claude, Claude Code, Cursor, ChatGPT, Codex and Lovable.

Follow the guide to use Amplitude MCP with Claude Code, or check the Amplitude MCP docs for setup in each client.

"We've been able to embed our domain knowledge directly into our LLM workflows. Now insights that used to require specialized analytics expertise are accessible to everyone," says Moss Pauly, Technical Product Manager, Data and AI at Zip, describing how Zip uses Amplitude MCP.

The limitation is that answers depend on what you track. If your instrumentation is thin, the answers will be too, so you may want to add analytics with the Codex CLI first. For a deeper comparison, see our guide to the best MCP servers for product analytics.

Pricing: Included on every Amplitude plan, including the Free plan, as of October 2026.

Best for: Developers and PMs who want to see what users did with the last release without leaving the editor.

2. Statsig MCP for experiments and feature flags

Statsig is an experimentation and feature flag platform. Its MCP server brings gates, experiments and dynamic configs into your coding session, which covers the ship step: release behind a flag, then roll out.

Statsig builds the server and documents it itself. It works in Codex, ChatGPT, Cursor and Claude Code. The assistant can read, create and update gates, experiments and dynamic configs. Tool visibility follows your Statsig permissions, so the agent can't do more than your account allows.

Write access is the main benefit and also the main risk. An agent that updates a gate can change what production users see, so review each change before you approve it.

Best for: Statsig customers who want to create and manage flags as part of the coding task.

3. GitHub MCP for repos, issues and pull requests

The official GitHub MCP server lets the assistant read code across repos, search and open issues, review pull requests and check workflow runs. Most stacks start here because so much of the build loop already lives in GitHub.

GitHub maintains the server. You can use the hosted version with OAuth or run it locally with a personal access token. Toolsets let you turn on only the groups you need, and a read-only mode stops the agent from writing anything.

With every toolset on, it exposes a long list of tools that takes up the model's context. Turn on repos and pull requests first and add more only when a task calls for them.

Best for: Any developer whose code, issues and reviews live on GitHub.

4. Context7 MCP for up-to-date library docs

Upstash's Context7 pulls current, version-specific docs and code examples into the prompt. Your assistant stops writing against an API that changed two releases ago.

Upstash maintains it as an open-source project, and it runs hosted or locally. Because it only reads public documentation, it can't change anything in your systems, and there's no account for an agent to misuse. An optional API key raises rate limits. If you add one, keep it in your client's secret settings, not a shared config.

Coverage depends on what's indexed. Niche or internal libraries may be thin or missing, and in that case the model falls back on its training data.

Best for: Builders working with fast-moving frameworks and SDKs.

5. Playwright MCP for browser testing and automation

Microsoft's Playwright MCP gives the assistant a real browser. It navigates, clicks and fills forms, and it reads pages through structured accessibility snapshots rather than screenshots, which keeps runs fast and easy for the model to read.

Microsoft maintains it, and it runs locally with no account to connect. The main permission question is what the browser can reach.

That's also the catch: a logged-in session gives the agent the same access you have. Run it with a clean browser profile or a test account so the agent can't act on your real sessions.

Best for: Checking UI changes end to end before you open a pull request.

6. Sentry MCP for errors and debugging

Sentry's MCP server brings issues, stack traces and error context into the assistant. The agent can work from the real error toward its cause without you pasting logs into the chat.

Sentry maintains it as a hosted server. Sign in with OAuth, and the server sees only the organizations and projects your Sentry account can.

Sentry tells you what broke and where. It won't show whether the bug cost you conversions or retention, which is where product data helps.

Best for: On-call engineers who triage production errors daily.

7. Figma MCP for design to code

Code generated from a screenshot is a guess. Figma's MCP server replaces the guess with structured design context: frames, components, variables and layout data.

Figma offers it two ways: a local server through the desktop app and a hosted remote server. Both sign in through your Figma account, and the assistant reads design data rather than editing files.

Output quality follows file quality. Well-named components and auto layout produce clean code, and messy files produce messy code.

Best for: Front-end developers turning designs into components.

8. Linear MCP for issues and planning

At the start of a task, Linear's MCP server can pull the ticket, its project and its comments into context. It can also find, create and update issues, projects and comments, which covers the plan step.

Linear runs the server for you. You authorize it with OAuth, and the agent acts as you inside your workspace permissions.

An agent with write access can also create clutter: duplicate issues, noisy comments or status changes nobody asked for. Ask it to draft changes before it posts them.

Best for: Pulling ticket context into the session before you write code, if you plan in Linear.

9. Supabase MCP for database and backend

The Supabase MCP server connects the assistant to your Supabase project. It can query tables, design schemas, run migrations, fetch logs and generate types.

Supabase maintains it and documents clear guardrails: a read-only mode, scoping to a single project and advice to keep it away from production data.

Even on a dev project, a bad migration can drop tables you need, so review every migration before it runs.

Best for: Builders shipping full-stack apps on Supabase.

10. Notion MCP for specs and team knowledge

Specs, product requirement docs and runbooks become context the agent can pull in through Notion's MCP server, which can search, read, create and update pages.

Notion hosts and maintains it. Connecting takes one OAuth sign-in, and the agent sees exactly the pages your account can.

The agent trusts what it reads. Outdated specs lead to confident, outdated code, so give it the current pages first.

Best for: Organizations whose specs and decisions live in Notion.

Most of the loop helps you build the feature right. The measure step tells you whether it was worth building. Amplitude MCP answers that question by working with your existing Amplitude charts, dashboards, experiments, cohorts, events and properties, under your own account permissions.

It also combines with the rest of your stack. An agent can check why conversion dropped after a release, then open an issue in your tracker, all in one session.

Build your stack around the steps in your loop, not around every tool you pay for.

• Plan: Linear for the ticket, Notion for the spec.
• Design: Figma for components and layout.
• Build: GitHub for code, Context7 for docs and Supabase for the backend.
• Test: Playwright to run the flow in a browser.
• Ship and fix: Sentry for errors, Statsig for flags and rollouts.
• Measure: Amplitude for what users did next.

Each server adds tool definitions to the model's context, so turn on only the servers your current task needs and switch others on when the work moves to a new step. Toolsets and read-only modes trim the tool count further.

Yes, when the server gives the agent something it can't get on its own: data behind a login, such as product analytics, error logs or design files. A good server also handles OAuth for you, returns structured results the model can reason over and limits what the agent can touch.

Not every job needs one. If a mature CLI exists, such as git, the model may already know how to use it, and a server only adds context weight. The same applies to GitHub's server if you already live in the gh CLI. In scripts and CI pipelines where no model is involved, a direct API call is simpler.

METR's early-2025 study found experienced open-source developers worked slower with AI tools. In a 2026 update to its developer productivity research, METR said developers are likely "more sped up from AI tools now" than a year earlier, though it calls that weak evidence. With the Cui study, the evidence is positive but noisy, so measure your own results.

You don't need all 10 on day one. Connect one server per step, starting with the step you can't see today. For most teams, that's what users do after a release. Once that's in place, you can tell whether the code you shipped moved the metric you cared about.

For more workflows, browse the Amplitude build guides.